LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

Saravanakumar Kandasamy () and Aswani Kumar Cherukuri ()
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Saravanakumar Kandasamy: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
Aswani Kumar Cherukuri: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India

Journal of Information & Knowledge Management (JIKM), 2021, vol. 20, issue 01, 1-21

Abstract: Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

Keywords: Lesk similarity; distributional semantic similarity; gloss-based similarity measure; WordNet; computational linguistics (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0219649221500064

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